Add kmeans_mnist.ipynb

files/dl/apprentissage/cours/kmeans/kmeans_mnist.ipynb

@@ -0,0 +1,71 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "from sklearn.cluster import KMeans\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "kmeans = KMeans(n_clusters=10, random_state=0).fit(digits.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 5, figsize=(10, 4))\n",
+    "for i, c in enumerate(kmeans.cluster_centers_):\n",
+    "    axes[i // 5 , i % 5].imshow(c.reshape(8, 8), cmap=plt.cm.binary)\n",
+    "    axes[i // 5, i % 5].axis('off')\n",
+    "# for c in range(10):\n",
+    "#     axes[1, c].imshow(digits.data[kmeans.labels_ == c][0].reshape(8, 8), cmap=plt.cm.binary)\n",
+    "#     axes[1, c].axis('off')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}